1.0 TITLE¶

Obesity level prediction in individuals with the use of classification algorithm for building recommender systems¶

In [ ]:

2.0 INTRODUCTION¶

PROBLEM STATEMENT¶

Predicting obesity level of individuals given eating habits and physical conditions.¶

Classification Algorithm will be used to achieve this by experimenting with KNN and Decision Trees.¶

3.0 DATASETS¶

3.1 DATASET INFORMATION¶

This dataset include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia,¶

Based on their eating habits and physical condition.¶

The data contains 17 attributes and 2111 records,¶

The records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using:¶

The values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III.¶

3.2 DATASET SOURCE¶

https://archive.ics.uci.edu/ml/datasets/Estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition+

3.3 LIBRARIES¶

In [1]:
# Importing Libraries

import numpy as np
import pandas as pd
import sklearn as sk
import matplotlib.pyplot as plt
import seaborn as sns

4.0 EXPLANATION AND PREPARATION¶

OF DATA¶

Installing data prep¶

In [2]:
pip install -U dataprep

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Note: you may need to restart the kernel to use updated packages.

4.1 LOADING THE DATASET¶

In [3]:
#Loading the Dataset
Obesity_data = pd.read_csv('Obesitydataset.csv')

Using dataprep to get an understanding of the dataset¶

In [4]:
from dataprep.eda import plot
plot(Obesity_data)

  0%|                                                                                          | 0/755 [00:00<…
Out[4]:
DataPrep.EDA Report
Dataset Statistics
Number of Variables 17
Number of Rows 2111
Missing Cells 0
Missing Cells (%) 0.0%
Duplicate Rows 24
Duplicate Rows (%) 1.1%
Total Size in Memory 1.3 MB
Average Row Size in Memory 641.9 B
Variable Types
  • Categorical: 9
  • Numerical: 8
Dataset Insights
FCVC is skewed Skewed
NCP is skewed Skewed
CH2O is skewed Skewed
FAF is skewed Skewed
TUE is skewed Skewed
Dataset has 24 (1.14%) duplicate rows Duplicates
FAF has 411 (19.47%) zeros Zeros
TUE has 557 (26.39%) zeros Zeros

Number of plots per page:

Gender
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
Age
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Height
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Weight
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
family_history_with_overweight
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
FAVC
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
FCVC
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. FCVC is skewed
NCP
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. NCP is skewed
CAEC
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
SMOKE
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
CH2O
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. CH2O is skewed
SCC
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
FAF
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. FAF is skewed
  2. FAF has 411 (19.47%) zeros
TUE
'hist.bins': 50
Number of bins in the histogram
'hist.yscale': 'linear'
Y-axis scale ("linear" or "log")
'hist.color': '#aec7e8'
Color
Column Insights
  1. TUE is skewed
  2. TUE has 557 (26.39%) zeros
CALC
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
MTRANS
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
NObeyesdad
'bar.bars': 10
Maximum number of bars to display
'bar.sort_descending': True
Whether to sort the bars in descending order
'bar.yscale': 'linear'
Y-axis scale ("linear" or "log")
In [5]:
### At a glace, we can observe the following from the graph:
In [6]:
from dataprep.eda import plot_correlation
plot_correlation(Obesity_data)
Out[6]:
DataPrep.EDA Report
Pearson Spearman KendallTau
Highest Positive Correlation 0.463 0.463 0.322
Highest Negative Correlation -0.297 -0.298 -0.214
Lowest Correlation 0.012 0.003 0.002
Mean Correlation 0.056 0.063 0.045
'height': 400
Height of the plot
'width': 400
Width of the plot
  • Most positive correlated: (Height, Weight)
  • Most negative correlated: (Age, TUE)
  • Least correlated: (CH2O, TUE)
'height': 400
Height of the plot
'width': 400
Width of the plot
  • Most positive correlated: (Height, Weight)
  • Most negative correlated: (Age, TUE)
  • Least correlated: (Weight, NCP)
'height': 400
Height of the plot
'width': 400
Width of the plot
  • Most positive correlated: (Height, Weight)
  • Most negative correlated: (Age, TUE)
  • Least correlated: (Age, Height)

4.2 ANALYZING THE DATASET¶

Total number of rows and columns¶

In [7]:
Obesity_data.shape
Out[7]:
(2111, 17)

Checking the first 5 rows of the data¶

In [8]:
Obesity_data.head()
Out[8]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
0 Female 21.0 1.62 64.0 yes no 2.0 3.0 Sometimes no 2.0 no 0.0 1.0 no Public_Transportation Normal_Weight
1 Female 21.0 1.52 56.0 yes no 3.0 3.0 Sometimes yes 3.0 yes 3.0 0.0 Sometimes Public_Transportation Normal_Weight
2 Male 23.0 1.80 77.0 yes no 2.0 3.0 Sometimes no 2.0 no 2.0 1.0 Frequently Public_Transportation Normal_Weight
3 Male 27.0 1.80 87.0 no no 3.0 3.0 Sometimes no 2.0 no 2.0 0.0 Frequently Walking Overweight_Level_I
4 Male 22.0 1.78 89.8 no no 2.0 1.0 Sometimes no 2.0 no 0.0 0.0 Sometimes Public_Transportation Overweight_Level_II

Checking the last 5 rows of the data¶

In [9]:
Obesity_data.tail()
Out[9]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
2106 Female 20.976842 1.710730 131.408528 yes yes 3.0 3.0 Sometimes no 1.728139 no 1.676269 0.906247 Sometimes Public_Transportation Obesity_Type_III
2107 Female 21.982942 1.748584 133.742943 yes yes 3.0 3.0 Sometimes no 2.005130 no 1.341390 0.599270 Sometimes Public_Transportation Obesity_Type_III
2108 Female 22.524036 1.752206 133.689352 yes yes 3.0 3.0 Sometimes no 2.054193 no 1.414209 0.646288 Sometimes Public_Transportation Obesity_Type_III
2109 Female 24.361936 1.739450 133.346641 yes yes 3.0 3.0 Sometimes no 2.852339 no 1.139107 0.586035 Sometimes Public_Transportation Obesity_Type_III
2110 Female 23.664709 1.738836 133.472641 yes yes 3.0 3.0 Sometimes no 2.863513 no 1.026452 0.714137 Sometimes Public_Transportation Obesity_Type_III

Dataset Information¶

In [10]:
Obesity_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2111 entries, 0 to 2110
Data columns (total 17 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   Gender                          2111 non-null   object 
 1   Age                             2111 non-null   float64
 2   Height                          2111 non-null   float64
 3   Weight                          2111 non-null   float64
 4   family_history_with_overweight  2111 non-null   object 
 5   FAVC                            2111 non-null   object 
 6   FCVC                            2111 non-null   float64
 7   NCP                             2111 non-null   float64
 8   CAEC                            2111 non-null   object 
 9   SMOKE                           2111 non-null   object 
 10  CH2O                            2111 non-null   float64
 11  SCC                             2111 non-null   object 
 12  FAF                             2111 non-null   float64
 13  TUE                             2111 non-null   float64
 14  CALC                            2111 non-null   object 
 15  MTRANS                          2111 non-null   object 
 16  NObeyesdad                      2111 non-null   object 
dtypes: float64(8), object(9)
memory usage: 280.5+ KB

Description of the dataset¶

In [11]:
Obesity_data.describe()
Out[11]:
Age Height Weight FCVC NCP CH2O FAF TUE
count 2111.000000 2111.000000 2111.000000 2111.000000 2111.000000 2111.000000 2111.000000 2111.000000
mean 24.312600 1.701677 86.586058 2.419043 2.685628 2.008011 1.010298 0.657866
std 6.345968 0.093305 26.191172 0.533927 0.778039 0.612953 0.850592 0.608927
min 14.000000 1.450000 39.000000 1.000000 1.000000 1.000000 0.000000 0.000000
25% 19.947192 1.630000 65.473343 2.000000 2.658738 1.584812 0.124505 0.000000
50% 22.777890 1.700499 83.000000 2.385502 3.000000 2.000000 1.000000 0.625350
75% 26.000000 1.768464 107.430682 3.000000 3.000000 2.477420 1.666678 1.000000
max 61.000000 1.980000 173.000000 3.000000 4.000000 3.000000 3.000000 2.000000

4.3 Exploratory Data Analysis (EDA)¶

Contents of the dataset Obesity_data¶

In [12]:
Obesity_data.columns
Out[12]:
Index(['Gender', 'Age', 'Height', 'Weight', 'family_history_with_overweight',
       'FAVC', 'FCVC', 'NCP', 'CAEC', 'SMOKE', 'CH2O', 'SCC', 'FAF', 'TUE',
       'CALC', 'MTRANS', 'NObeyesdad'],
      dtype='object')

Unique Value Count¶

In [13]:
#Counting the unique items in each column heading 
for col in Obesity_data.columns:
    print(f'{col} -- ({Obesity_data[col].nunique()} unique items):') # print the column name and "unique items" as the next column
    print(f'{Obesity_data[col].value_counts()}') #Count the unique items in each column in descending order
    print('')       # Put a space between each column heading
    print('-' * 30) #Print a line to seperate each item

Gender -- (2 unique items):
Male      1068
Female    1043
Name: Gender, dtype: int64

------------------------------
Age -- (1402 unique items):
18.000000    128
26.000000    101
21.000000     96
23.000000     89
19.000000     59
            ... 
23.320120      1
34.243146      1
18.549437      1
36.310292      1
23.664709      1
Name: Age, Length: 1402, dtype: int64

------------------------------
Height -- (1574 unique items):
1.700000    60
1.650000    50
1.600000    43
1.750000    39
1.620000    36
            ..
1.842943     1
1.706082     1
1.704141     1
1.705813     1
1.738836     1
Name: Height, Length: 1574, dtype: int64

------------------------------
Weight -- (1525 unique items):
80.000000     59
70.000000     43
50.000000     42
75.000000     40
60.000000     37
              ..
65.140408      1
87.279890      1
67.083121      1
90.138680      1
133.472641     1
Name: Weight, Length: 1525, dtype: int64

------------------------------
family_history_with_overweight -- (2 unique items):
yes    1726
no      385
Name: family_history_with_overweight, dtype: int64

------------------------------
FAVC -- (2 unique items):
yes    1866
no      245
Name: FAVC, dtype: int64

------------------------------
FCVC -- (810 unique items):
3.000000    652
2.000000    600
1.000000     33
2.823179      2
2.214980      2
           ... 
2.927409      1
2.706134      1
2.010684      1
2.300408      1
2.680375      1
Name: FCVC, Length: 810, dtype: int64

------------------------------
NCP -- (635 unique items):
3.000000    1203
1.000000     199
4.000000      69
2.776840       2
3.985442       2
            ... 
3.054899       1
3.118013       1
3.335876       1
3.205009       1
1.089048       1
Name: NCP, Length: 635, dtype: int64

------------------------------
CAEC -- (4 unique items):
Sometimes     1765
Frequently     242
Always          53
no              51
Name: CAEC, dtype: int64

------------------------------
SMOKE -- (2 unique items):
no     2067
yes      44
Name: SMOKE, dtype: int64

------------------------------
CH2O -- (1268 unique items):
2.000000    448
1.000000    211
3.000000    162
2.825629      3
1.636326      3
           ... 
1.622638      1
2.452986      1
2.035954      1
1.944095      1
2.863513      1
Name: CH2O, Length: 1268, dtype: int64

------------------------------
SCC -- (2 unique items):
no     2015
yes      96
Name: SCC, dtype: int64

------------------------------
FAF -- (1190 unique items):
0.000000    411
1.000000    234
2.000000    183
3.000000     75
0.110174      2
           ... 
1.916751      1
0.954459      1
0.340915      1
0.986414      1
1.026452      1
Name: FAF, Length: 1190, dtype: int64

------------------------------
TUE -- (1129 unique items):
0.000000    557
1.000000    292
2.000000    109
0.630866      4
1.119877      3
           ... 
1.343044      1
1.019452      1
0.673408      1
0.997600      1
0.714137      1
Name: TUE, Length: 1129, dtype: int64

------------------------------
CALC -- (4 unique items):
Sometimes     1401
no             639
Frequently      70
Always           1
Name: CALC, dtype: int64

------------------------------
MTRANS -- (5 unique items):
Public_Transportation    1580
Automobile                457
Walking                    56
Motorbike                  11
Bike                        7
Name: MTRANS, dtype: int64

------------------------------
NObeyesdad -- (7 unique items):
Obesity_Type_I         351
Obesity_Type_III       324
Obesity_Type_II        297
Overweight_Level_I     290
Overweight_Level_II    290
Normal_Weight          287
Insufficient_Weight    272
Name: NObeyesdad, dtype: int64

------------------------------

Mean values group by NObeyesdad¶

In [14]:
Obesity_data.groupby('NObeyesdad').mean()
Out[14]:
Age Height Weight FCVC NCP CH2O FAF TUE
NObeyesdad
Insufficient_Weight 19.783237 1.691117 49.906330 2.480788 2.914403 1.871281 1.250131 0.839459
Normal_Weight 21.738676 1.676585 62.155052 2.334495 2.738676 1.850174 1.247387 0.675958
Obesity_Type_I 25.884941 1.693804 92.870198 2.186050 2.431862 2.112218 0.986748 0.676743
Obesity_Type_II 28.233785 1.771795 115.305311 2.391284 2.744555 1.877658 0.971857 0.515186
Obesity_Type_III 23.495554 1.687559 120.941114 3.000000 3.000000 2.208493 0.664817 0.604623
Overweight_Level_I 23.417674 1.687836 74.266828 2.264631 2.504218 2.058725 1.056796 0.612992
Overweight_Level_II 26.996981 1.703748 82.085271 2.260578 2.495529 2.025133 0.958072 0.697275

4.4 Creating EDA report¶

In [15]:
from dataprep.eda import create_report
report=create_report(Obesity_data)

  0%|                                                                                         | 0/2397 [00:00<…
In [16]:
#report.show()
In [17]:
report.save('Report')

Report has been saved to Report.html!
In [18]:
report.show_browser()

The Obesity_data Report will show in the browser below using browser()¶

file:///C:/Users/alade/AppData/Local/Temp/tmpc89sxhcd.html¶

4.5 Missing Value¶

In [19]:
from dataprep.eda import plot_missing
plot_missing(Obesity_data)

  0%|                                                                                          | 0/198 [00:00<…
Out[19]:
DataPrep.EDA Report

Missing Statistics

Missing Cells0
Missing Cells (%)0.0%
Missing Columns0
Missing Rows0
Avg Missing Cells per Column0.0
Avg Missing Cells per Row0.0
'height': 500
Height of the plot
'width': 500
Width of the plot
'spectrum.bins': 20
Number of bins
'height': 500
Height of the plot
'width': 500
Width of the plot
'height': 500
Height of the plot
'width': 500
Width of the plot
'height': 500
Height of the plot
'width': 500
Width of the plot
In [20]:
#Checking for null values
Obesity_data.isnull().sum()
Out[20]:
Gender                            0
Age                               0
Height                            0
Weight                            0
family_history_with_overweight    0
FAVC                              0
FCVC                              0
NCP                               0
CAEC                              0
SMOKE                             0
CH2O                              0
SCC                               0
FAF                               0
TUE                               0
CALC                              0
MTRANS                            0
NObeyesdad                        0
dtype: int64

NOTE¶

There are no missing values in Obesity_data, but if there are missing values, the SimpleInputer can be used to estimate null value.¶

Numerical Values¶

If for example Height and Weight has Null values, then we can use the code below to input missing value using either the mean median most_frequent constant

import numpy as np numimputer =SimpleImputer(missing_values=np.nan, strategy="mean" numimputer=numimputer.fit(df[["Height","Weight"]]) new_value=numImputer.transform(df[["Height","Weight"]]) new_value

The "Strategy" will be interchanged with median, most-Frequent or constant if any of these is to be used.

Categorical Values¶

If it is a categorical Variable then most_frequent or constant will be used. Assuming SMOKE and CALC have missing values

import numpy as np catimputer=SimpleImputer(missing_values=np.nan,strategy='most_frequent') catimputer=catimputer.fit(df[["SMOKE","CALC"]]) new_value=catImputer.transform(df[["SMOKE","CALC"]]) new_value

4.6 Duplicate Rows¶

NOTE¶

There are duplicate rows 24 in number as shown on the EDA report¶

They need to be dropped because they are not useful for analysis¶

In [21]:
Obesity_data.duplicated().sum()
Out[21]:
24

Checking the duplicated rows¶

In [22]:
Obesity_data.loc[Obesity_data.duplicated(keep='first'),:]
Out[22]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
98 Female 21.0 1.52 42.0 no no 3.0 1.0 Frequently no 1.0 no 0.0 0.0 Sometimes Public_Transportation Insufficient_Weight
106 Female 25.0 1.57 55.0 no yes 2.0 1.0 Sometimes no 2.0 no 2.0 0.0 Sometimes Public_Transportation Normal_Weight
174 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
179 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
184 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
209 Female 22.0 1.69 65.0 yes yes 2.0 3.0 Sometimes no 2.0 no 1.0 1.0 Sometimes Public_Transportation Normal_Weight
309 Female 16.0 1.66 58.0 no no 2.0 1.0 Sometimes no 1.0 no 0.0 1.0 no Walking Normal_Weight
460 Female 18.0 1.62 55.0 yes yes 2.0 3.0 Frequently no 1.0 no 1.0 1.0 no Public_Transportation Normal_Weight
467 Male 22.0 1.74 75.0 yes yes 3.0 3.0 Frequently no 1.0 no 1.0 0.0 no Automobile Normal_Weight
496 Male 18.0 1.72 53.0 yes yes 2.0 3.0 Sometimes no 2.0 no 0.0 2.0 Sometimes Public_Transportation Insufficient_Weight
527 Female 21.0 1.52 42.0 no yes 3.0 1.0 Frequently no 1.0 no 0.0 0.0 Sometimes Public_Transportation Insufficient_Weight
659 Female 21.0 1.52 42.0 no yes 3.0 1.0 Frequently no 1.0 no 0.0 0.0 Sometimes Public_Transportation Insufficient_Weight
663 Female 21.0 1.52 42.0 no yes 3.0 1.0 Frequently no 1.0 no 0.0 0.0 Sometimes Public_Transportation Insufficient_Weight
763 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
764 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
824 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
830 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
831 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
832 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
833 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
834 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
921 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
922 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I
923 Male 21.0 1.62 70.0 no yes 2.0 1.0 no no 3.0 no 1.0 0.0 Sometimes Public_Transportation Overweight_Level_I

Dropping duplicate rows¶

In [23]:
Obesity_data.drop_duplicates(keep='first').shape
Out[23]:
(2087, 17)
In [24]:
Obesity_data
Out[24]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
0 Female 21.000000 1.620000 64.000000 yes no 2.0 3.0 Sometimes no 2.000000 no 0.000000 1.000000 no Public_Transportation Normal_Weight
1 Female 21.000000 1.520000 56.000000 yes no 3.0 3.0 Sometimes yes 3.000000 yes 3.000000 0.000000 Sometimes Public_Transportation Normal_Weight
2 Male 23.000000 1.800000 77.000000 yes no 2.0 3.0 Sometimes no 2.000000 no 2.000000 1.000000 Frequently Public_Transportation Normal_Weight
3 Male 27.000000 1.800000 87.000000 no no 3.0 3.0 Sometimes no 2.000000 no 2.000000 0.000000 Frequently Walking Overweight_Level_I
4 Male 22.000000 1.780000 89.800000 no no 2.0 1.0 Sometimes no 2.000000 no 0.000000 0.000000 Sometimes Public_Transportation Overweight_Level_II
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2106 Female 20.976842 1.710730 131.408528 yes yes 3.0 3.0 Sometimes no 1.728139 no 1.676269 0.906247 Sometimes Public_Transportation Obesity_Type_III
2107 Female 21.982942 1.748584 133.742943 yes yes 3.0 3.0 Sometimes no 2.005130 no 1.341390 0.599270 Sometimes Public_Transportation Obesity_Type_III
2108 Female 22.524036 1.752206 133.689352 yes yes 3.0 3.0 Sometimes no 2.054193 no 1.414209 0.646288 Sometimes Public_Transportation Obesity_Type_III
2109 Female 24.361936 1.739450 133.346641 yes yes 3.0 3.0 Sometimes no 2.852339 no 1.139107 0.586035 Sometimes Public_Transportation Obesity_Type_III
2110 Female 23.664709 1.738836 133.472641 yes yes 3.0 3.0 Sometimes no 2.863513 no 1.026452 0.714137 Sometimes Public_Transportation Obesity_Type_III

2111 rows × 17 columns

4.7 EDA - Understanding the impact of the dependent variable(Obesity level) on independent variables¶

Obesity level based on Age¶

In [25]:
plt.figure(figsize=(10,5))
plt.title("Obesity level based on Age")
sns.histplot(x="Age", hue = "NObeyesdad", data=Obesity_data)
plt.show()

This visualization shows that Obesity_Type _III is prevalent in the age bracket of 26 years¶

Obesity level based on Gender¶

In [26]:
plt.figure(figsize=(10,5))
plt.title("Obesity level based on Gender")
sns.histplot(x="Gender", hue = "NObeyesdad", data=Obesity_data)
plt.show()

This visualization shows that obesity_type_III is prevalent in the female gender¶

Obesity level based on family_history_with_overweight graph¶

In [27]:
plt.figure(figsize=(10,5))
plt.title("Obesity level based on family_history_with_overweight")
sns.histplot(x="family_history_with_overweight", hue = "NObeyesdad", data=Obesity_data)
plt.show()

This visualization shows that most people have an history with overweight¶

Obesity level based on Transportation System graph¶

In [28]:
plt.figure(figsize=(10,5))
plt.title("Obesity level based on Transportation System")
sns.histplot(x="MTRANS", hue = "NObeyesdad", data=Obesity_data)
plt.show()

This visualization shows that walking is associated with normal weight while other means of transport is associated with other types of obesity¶

"Obesity Attribute based on Monitoring of Calorie Intake" graph¶

In [29]:
plt.figure(figsize=(10,5))
plt.title("Obesity Attribute based on Monitoring of Calorie Intake")
sns.histplot(x="SCC", hue = "NObeyesdad", data=Obesity_data)
plt.show()

This visualization shows that most people do not monitor their calorie intake¶

"Obesity Attribute based on frequency of consumption of high calorific food" graph¶

In [30]:
plt.figure(figsize=(10,5))
plt.title("Obesity Attribute based on frequency of consumption of high calorific food")
sns.histplot(x="FAVC", hue = "NObeyesdad", data=Obesity_data)
plt.show()

"Obesity Attribute based on frequency of consumption of Alcohol" graph¶

In [31]:
plt.figure(figsize=(10,5))
plt.title("Obesity Attribute based on frequency of consumption of Alcohol")
sns.histplot(x="CALC", hue = "NObeyesdad", data=Obesity_data)
plt.show()

This visualization shows that alcohol intake does no necessarily mean obesity. Infact, people with none or sparse consumption are more prone to obesity¶

"Obesity Attribute based on frequency of Smoking" graph¶

In [32]:
plt.figure(figsize=(10,5))
plt.title("Obesity Attribute based on frequency of Smoking")
sns.histplot(x="SMOKE", hue = "NObeyesdad", data=Obesity_data)
plt.show()

5.0 IMPLEMENTATION IN PYTHON¶

5.1 Class Imbalance¶

In [33]:
sns.countplot(Obesity_data['NObeyesdad'])

C:\Users\alade\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
Out[33]:
<AxesSubplot:xlabel='NObeyesdad', ylabel='count'>

5.2 Normalization¶

Uniforms Feature values with different range so that the range of the variables is consistent i.e features are within the same scale¶

Normalizing the dataset caused error, so this data will not be normalised but the code is above¶

In [34]:
#Normalisation can be done either with -Simple Feature Scaling
#Simple Feature Scaling
#Min-Max
#Z-Score
#Below is an example of Simple Feature Scaling
#Obesity_data['Age']= Obesity_data['Age']/Obesity_data['Age'].max()
In [35]:
Obesity_data.Age
Out[35]:
0       21.000000
1       21.000000
2       23.000000
3       27.000000
4       22.000000
          ...    
2106    20.976842
2107    21.982942
2108    22.524036
2109    24.361936
2110    23.664709
Name: Age, Length: 2111, dtype: float64
In [36]:
#from sklearn.preprocessing import MinMaxScaler
#scaler=MinMaxScaler()
#Obesity_data = scaler.fit_transform(Obesity_data)

Data mapping¶

NObeyesdad column is the class dependant variable while the other columns are the independent variables¶

5.3 Changing Categorical Variables to Numerical¶

Variables¶

Age¶

Gender¶

family_history_with_overweight-¶

MTRANS-Transportation system¶

SCC- Monitoring of Calorie intake¶

FAVC-Frequency of consumption of high calorific food¶

CAEC- Consumption of food between meals¶

CALC-Consumption of Alchol¶

SMOKE-Does the individual smoke or not¶

NObeyesdad- Estimation of obesity levels in individuals¶

In [37]:
Obesity_data.loc[:,["Age","Gender","family_history_with_overweight","MTRANS","SCC","FAVC","CALC","SMOKE","NObeyesdad"]]
Out[37]:
Age Gender family_history_with_overweight MTRANS SCC FAVC CALC SMOKE NObeyesdad
0 21.000000 Female yes Public_Transportation no no no no Normal_Weight
1 21.000000 Female yes Public_Transportation yes no Sometimes yes Normal_Weight
2 23.000000 Male yes Public_Transportation no no Frequently no Normal_Weight
3 27.000000 Male no Walking no no Frequently no Overweight_Level_I
4 22.000000 Male no Public_Transportation no no Sometimes no Overweight_Level_II
... ... ... ... ... ... ... ... ... ...
2106 20.976842 Female yes Public_Transportation no yes Sometimes no Obesity_Type_III
2107 21.982942 Female yes Public_Transportation no yes Sometimes no Obesity_Type_III
2108 22.524036 Female yes Public_Transportation no yes Sometimes no Obesity_Type_III
2109 24.361936 Female yes Public_Transportation no yes Sometimes no Obesity_Type_III
2110 23.664709 Female yes Public_Transportation no yes Sometimes no Obesity_Type_III

2111 rows × 9 columns

In converting categorical data to numeric, replace and map will be used to show different ways it can be changed¶

The objective is to classify the data and predict the obesity levels in individuals with a high level of accuracy¶

Gender¶

In [38]:
#print(Obesity_data['Gender'].nunique())
#Obesity_data['Gender'].value_counts()
In [39]:
Obesity_data["Gender"]=Obesity_data["Gender"].replace(["Male","Female"],[0,1])
In [40]:
Obesity_data.head()
Out[40]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
0 1 21.0 1.62 64.0 yes no 2.0 3.0 Sometimes no 2.0 no 0.0 1.0 no Public_Transportation Normal_Weight
1 1 21.0 1.52 56.0 yes no 3.0 3.0 Sometimes yes 3.0 yes 3.0 0.0 Sometimes Public_Transportation Normal_Weight
2 0 23.0 1.80 77.0 yes no 2.0 3.0 Sometimes no 2.0 no 2.0 1.0 Frequently Public_Transportation Normal_Weight
3 0 27.0 1.80 87.0 no no 3.0 3.0 Sometimes no 2.0 no 2.0 0.0 Frequently Walking Overweight_Level_I
4 0 22.0 1.78 89.8 no no 2.0 1.0 Sometimes no 2.0 no 0.0 0.0 Sometimes Public_Transportation Overweight_Level_II

family_history_with_overweight¶

In [41]:
#print(Obesity_data['family_history_with_overweight'].nunique())
#Obesity_data['family_history_with_overweight'].value_counts()
In [42]:
Obesity_data.family_history_with_overweight=Obesity_data.family_history_with_overweight.map({"yes":0,"no":1})

MTRANS- Transportation System¶

In [43]:
print(Obesity_data['MTRANS'].nunique())
Obesity_data['MTRANS'].value_counts()

5
Out[43]:
Public_Transportation    1580
Automobile                457
Walking                    56
Motorbike                  11
Bike                        7
Name: MTRANS, dtype: int64
In [44]:
Obesity_data.MTRANS=Obesity_data.MTRANS.map({"Public_Transportation":0,"Automobile":1,"Walking":2,"Motorbike":3,"Bike":4})

SCC - Monitoring of calorie Intake¶

In [45]:
print(Obesity_data['SCC'].nunique())
Obesity_data['SCC'].value_counts()

2
Out[45]:
no     2015
yes      96
Name: SCC, dtype: int64
In [46]:
Obesity_data.SCC=Obesity_data.SCC.map({"yes":0,"no":1})

FAVC- Frequency of consumption of high calorific food¶

In [47]:
print(Obesity_data['FAVC'].nunique())
Obesity_data['FAVC'].value_counts()

2
Out[47]:
yes    1866
no      245
Name: FAVC, dtype: int64
In [48]:
Obesity_data.FAVC=Obesity_data.FAVC.map({"yes":0,"no":1})

CAEC- Consumption of food between meals¶

In [49]:
print(Obesity_data['CAEC'].nunique())
Obesity_data['CAEC'].value_counts()

4
Out[49]:
Sometimes     1765
Frequently     242
Always          53
no              51
Name: CAEC, dtype: int64
In [50]:
Obesity_data.CAEC=Obesity_data.CAEC.map({"Sometimes":0,"no":1,"Frequently":2,"Always":3})

CALC -Consumption of Alchol¶

In [51]:
print(Obesity_data['CALC'].nunique())
Obesity_data['CALC'].value_counts()

4
Out[51]:
Sometimes     1401
no             639
Frequently      70
Always           1
Name: CALC, dtype: int64
In [52]:
Obesity_data.CALC=Obesity_data.CALC.map({"Sometimes":0,"no":1,"Frequently":2,"Always":3})

SMOKE-Does the individual smoke or not¶

In [53]:
print(Obesity_data['SMOKE'].nunique())
Obesity_data['SMOKE'].value_counts()

2
Out[53]:
no     2067
yes      44
Name: SMOKE, dtype: int64
In [54]:
Obesity_data.SMOKE=Obesity_data.SMOKE.map({"yes":0,"no":1})

NObeyesdad- Estimation of obesity level in individuals¶

In [55]:
print(Obesity_data['NObeyesdad'].nunique())
Obesity_data['NObeyesdad'].value_counts()

7
Out[55]:
Obesity_Type_I         351
Obesity_Type_III       324
Obesity_Type_II        297
Overweight_Level_I     290
Overweight_Level_II    290
Normal_Weight          287
Insufficient_Weight    272
Name: NObeyesdad, dtype: int64
In [56]:
Obesity_data.NObeyesdad=Obesity_data.NObeyesdad.map({"Obesity_Type_I":0,"Obesity_Type_II":1,"Obesity_Type_III":2,"Overweight_Level_I":3,"Overweight_Level_II":4,"Normal_Weight":5,"Insufficient_Weight":6})

All categoriacal data has been converted to numeric¶

In [57]:
Obesity_data.head()
Out[57]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
0 1 21.0 1.62 64.0 0 1 2.0 3.0 0 1 2.0 1 0.0 1.0 1 0 5
1 1 21.0 1.52 56.0 0 1 3.0 3.0 0 0 3.0 0 3.0 0.0 0 0 5
2 0 23.0 1.80 77.0 0 1 2.0 3.0 0 1 2.0 1 2.0 1.0 2 0 5
3 0 27.0 1.80 87.0 1 1 3.0 3.0 0 1 2.0 1 2.0 0.0 2 2 3
4 0 22.0 1.78 89.8 1 1 2.0 1.0 0 1 2.0 1 0.0 0.0 0 0 4
In [58]:
Obesity_data.describe().T
Out[58]:
count mean std min 25% 50% 75% max
Gender 2111.0 0.494079 0.500083 0.00 0.000000 0.000000 1.000000 1.00
Age 2111.0 24.312600 6.345968 14.00 19.947192 22.777890 26.000000 61.00
Height 2111.0 1.701677 0.093305 1.45 1.630000 1.700499 1.768464 1.98
Weight 2111.0 86.586058 26.191172 39.00 65.473343 83.000000 107.430682 173.00
family_history_with_overweight 2111.0 0.182378 0.386247 0.00 0.000000 0.000000 0.000000 1.00
FAVC 2111.0 0.116059 0.320371 0.00 0.000000 0.000000 0.000000 1.00
FCVC 2111.0 2.419043 0.533927 1.00 2.000000 2.385502 3.000000 3.00
NCP 2111.0 2.685628 0.778039 1.00 2.658738 3.000000 3.000000 4.00
CAEC 2111.0 0.328754 0.775161 0.00 0.000000 0.000000 0.000000 3.00
SMOKE 2111.0 0.979157 0.142893 0.00 1.000000 1.000000 1.000000 1.00
CH2O 2111.0 2.008011 0.612953 1.00 1.584812 2.000000 2.477420 3.00
SCC 2111.0 0.954524 0.208395 0.00 1.000000 1.000000 1.000000 1.00
FAF 2111.0 1.010298 0.850592 0.00 0.124505 1.000000 1.666678 3.00
TUE 2111.0 0.657866 0.608927 0.00 0.000000 0.625350 1.000000 2.00
CALC 2111.0 0.370441 0.550017 0.00 0.000000 0.000000 1.000000 3.00
MTRANS 2111.0 0.298437 0.577618 0.00 0.000000 0.000000 1.000000 4.00
NObeyesdad 2111.0 2.862151 2.009089 0.00 1.000000 3.000000 5.000000 6.00

Computation of pairwise correlation of all columns¶

5.4 Explanation of Experimental procedure¶

Heat Map showing correlation of variables¶

In [59]:
Obesity_corr=Obesity_data.corr()
Obesity_corr
Out[59]:
Gender Age Height Weight family_history_with_overweight FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC MTRANS NObeyesdad
Gender 1.000000 -0.048394 -0.618466 -0.161668 0.102512 0.064934 0.274505 -0.067600 0.077157 0.044698 -0.107930 -0.102633 -0.189607 -0.017269 -0.045436 -0.159592 0.119238
Age -0.048394 1.000000 -0.025958 0.202560 -0.205725 -0.063902 0.016291 -0.043944 -0.131644 -0.091987 -0.045304 0.116283 -0.144938 -0.296931 0.036711 0.405192 -0.301656
Height -0.618466 -0.025958 1.000000 0.463136 -0.247684 -0.178364 -0.038121 0.243672 -0.116778 -0.055499 0.213376 0.133753 0.294709 0.051912 -0.077203 0.083717 -0.129457
Weight -0.161668 0.202560 0.463136 1.000000 -0.496820 -0.272300 0.216125 0.107469 -0.391536 -0.025746 0.200575 0.201906 -0.051436 -0.071561 -0.238067 -0.088426 -0.721134
family_history_with_overweight 0.102512 -0.205725 -0.247684 -0.496820 1.000000 0.208036 -0.040372 -0.071370 0.303019 0.017385 -0.147437 -0.185422 0.056673 -0.022943 -0.014768 -0.008281 0.458095
FAVC 0.064934 -0.063902 -0.178364 -0.272300 0.208036 1.000000 0.027283 0.007000 0.187893 -0.050660 -0.009719 -0.190658 0.107995 -0.068417 0.137821 0.104705 0.273007
FCVC 0.274505 0.016291 -0.038121 0.216125 -0.040372 0.027283 1.000000 0.042216 0.008009 -0.014320 0.068461 -0.071852 0.019939 -0.101135 -0.078571 -0.050446 0.013033
NCP -0.067600 -0.043944 0.243672 0.107469 -0.071370 0.007000 0.042216 1.000000 0.026022 -0.007811 0.057088 0.015624 0.129504 0.036326 -0.095262 0.052452 0.088641
CAEC 0.077157 -0.131644 -0.116778 -0.391536 0.303019 0.187893 0.008009 0.026022 1.000000 -0.057912 -0.081347 -0.156784 0.046203 -0.011336 0.144413 0.017872 0.407684
SMOKE 0.044698 -0.091987 -0.055499 -0.025746 0.017385 -0.050660 -0.014320 -0.007811 -0.057912 1.000000 0.031995 0.047731 -0.011216 -0.017613 -0.022315 -0.027957 0.014750
CH2O -0.107930 -0.045304 0.213376 0.200575 -0.147437 -0.009719 0.068461 0.057088 -0.081347 0.031995 1.000000 -0.008036 0.167236 0.011965 -0.037409 -0.018297 -0.113323
SCC -0.102633 0.116283 0.133753 0.201906 -0.185422 -0.190658 -0.071852 0.015624 -0.156784 0.047731 -0.008036 1.000000 -0.074221 0.010928 -0.055562 -0.025002 -0.153079
FAF -0.189607 -0.144938 0.294709 -0.051436 0.056673 0.107995 0.019939 0.129504 0.046203 -0.011216 0.167236 -0.074221 1.000000 0.058562 0.134771 0.082912 0.132069
TUE -0.017269 -0.296931 0.051912 -0.071561 -0.022943 -0.068417 -0.101135 0.036326 -0.011336 -0.017613 0.011965 0.010928 0.058562 1.000000 0.112034 -0.110762 0.097669
CALC -0.045436 0.036711 -0.077203 -0.238067 -0.014768 0.137821 -0.078571 -0.095262 0.144413 -0.022315 -0.037409 -0.055562 0.134771 0.112034 1.000000 0.108336 0.082688
MTRANS -0.159592 0.405192 0.083717 -0.088426 -0.008281 0.104705 -0.050446 0.052452 0.017872 -0.027957 -0.018297 -0.025002 0.082912 -0.110762 0.108336 1.000000 0.034242
NObeyesdad 0.119238 -0.301656 -0.129457 -0.721134 0.458095 0.273007 0.013033 0.088641 0.407684 0.014750 -0.113323 -0.153079 0.132069 0.097669 0.082688 0.034242 1.000000
In [60]:
plt.figure(figsize=(12,10))
sns.heatmap(Obesity_corr, annot= True, cmap=plt.cm.CMRmap_r)
plt.title('Heatmap for Obesity_corr Correlation')
plt.show()

-ve1 indicates a perfectly negative linear correlation between two variables¶

0 indicates no linear correlation between two variables¶

+ve 1 indicates a perfectly positive linear correlation between two variables¶

5.5 Application of data mining techniques to dataset¶

Train and Test Dataset¶

The major aim of this experiment under classification algorithm is to predict obesity levels in individuals (x) based on their eating habits and physical features(y). Therefore the data needs to be sliced into input and output, then the training set and Test set¶

In [61]:
from sklearn.model_selection import train_test_split

X = Obesity_data.drop('NObeyesdad', axis = 1)
y = Obesity_data.NObeyesdad

X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 45, stratify=y)
In [62]:
X_train.shape, X_test.shape
Out[62]:
((1477, 16), (634, 16))

5.6 Normalisation¶

Scaling Feature¶

Different scales of different dimensions of variables can have an effect on algorithms, hence numerical inputs must be scaled to a standard range.¶

Train Data set X_train¶

In [63]:
#This scaling is done by subtracting the mean and dividing by the standard deviation
from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
X_train_ScaledObesityData=sc.fit_transform(X_train)
X_test_ScaledObesityData=sc.transform(X_test)

DECISION TREE¶

In [64]:
from sklearn import tree

tr = tree.DecisionTreeClassifier(random_state = 45)
tr.fit(X_train_ScaledObesityData, y_train)
y_pred = tr.predict(X_test_ScaledObesityData)
y_pred
Out[64]:
array([3, 6, 3, 0, 4, 4, 1, 0, 4, 6, 4, 6, 6, 3, 3, 5, 6, 1, 1, 3, 4, 2,
       0, 1, 5, 5, 4, 0, 4, 0, 2, 6, 5, 1, 5, 4, 3, 2, 4, 3, 6, 0, 3, 3,
       0, 1, 1, 5, 0, 1, 1, 1, 0, 5, 2, 2, 1, 0, 3, 4, 6, 6, 0, 0, 2, 5,
       1, 1, 2, 0, 0, 1, 6, 1, 2, 2, 1, 2, 5, 0, 3, 2, 1, 3, 1, 1, 4, 4,
       0, 3, 6, 1, 5, 5, 3, 5, 2, 5, 3, 3, 6, 4, 6, 0, 0, 5, 4, 6, 4, 4,
       3, 5, 4, 6, 6, 0, 5, 5, 1, 2, 6, 5, 1, 2, 2, 2, 4, 5, 6, 4, 0, 0,
       4, 4, 2, 2, 4, 4, 0, 4, 5, 4, 0, 5, 6, 5, 6, 3, 2, 3, 4, 3, 2, 6,
       3, 5, 5, 0, 6, 0, 5, 5, 5, 0, 0, 4, 1, 3, 2, 1, 6, 2, 2, 1, 4, 6,
       2, 5, 4, 5, 2, 0, 2, 5, 5, 6, 0, 4, 6, 6, 1, 4, 2, 4, 2, 2, 2, 2,
       5, 0, 1, 1, 1, 6, 2, 5, 2, 1, 5, 5, 6, 5, 5, 1, 0, 6, 4, 1, 3, 2,
       5, 1, 6, 4, 2, 1, 0, 0, 0, 4, 4, 2, 4, 0, 2, 0, 0, 4, 1, 4, 0, 2,
       6, 4, 1, 5, 2, 6, 4, 5, 4, 3, 6, 6, 3, 0, 5, 4, 6, 0, 4, 6, 4, 3,
       4, 4, 5, 2, 2, 2, 6, 1, 3, 2, 2, 1, 4, 2, 5, 0, 2, 4, 4, 0, 0, 1,
       3, 2, 6, 5, 6, 6, 6, 1, 1, 0, 3, 2, 2, 2, 3, 1, 5, 4, 6, 3, 5, 4,
       6, 6, 2, 5, 0, 6, 5, 4, 1, 0, 4, 0, 3, 4, 5, 1, 2, 5, 4, 0, 1, 3,
       1, 6, 4, 0, 4, 6, 5, 0, 6, 1, 0, 2, 0, 6, 3, 5, 0, 0, 3, 3, 5, 3,
       5, 0, 3, 3, 2, 6, 2, 5, 0, 1, 0, 0, 4, 1, 3, 1, 0, 5, 0, 3, 2, 6,
       3, 6, 2, 6, 5, 5, 5, 6, 4, 1, 3, 6, 5, 0, 1, 3, 6, 6, 0, 5, 0, 6,
       1, 2, 5, 2, 1, 1, 4, 6, 5, 4, 4, 3, 6, 6, 1, 4, 5, 0, 2, 3, 1, 6,
       1, 4, 1, 1, 0, 2, 4, 1, 5, 0, 5, 4, 3, 4, 2, 4, 5, 5, 0, 1, 6, 2,
       2, 5, 2, 2, 6, 5, 0, 0, 4, 4, 0, 4, 0, 3, 2, 3, 2, 1, 5, 1, 6, 3,
       4, 4, 1, 2, 0, 3, 1, 6, 2, 5, 3, 4, 1, 0, 6, 3, 6, 2, 0, 3, 6, 3,
       1, 0, 0, 3, 0, 5, 0, 0, 4, 4, 3, 5, 5, 2, 0, 3, 2, 4, 1, 2, 3, 4,
       1, 2, 0, 2, 2, 0, 2, 1, 5, 4, 5, 4, 0, 0, 0, 4, 1, 5, 3, 3, 6, 3,
       4, 0, 6, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 5, 6, 4, 3, 6, 3, 6, 6, 2,
       0, 0, 3, 0, 4, 0, 3, 6, 2, 3, 1, 0, 1, 0, 4, 6, 5, 6, 0, 0, 1, 5,
       4, 2, 3, 3, 5, 0, 2, 2, 2, 2, 2, 1, 6, 1, 0, 0, 0, 6, 2, 6, 5, 0,
       2, 1, 1, 5, 2, 1, 2, 2, 1, 2, 4, 4, 2, 3, 2, 5, 2, 1, 6, 4, 6, 5,
       6, 3, 1, 0, 2, 1, 0, 0, 0, 3, 4, 4, 6, 6, 5, 2, 1, 1], dtype=int64)

Accuracy of Training and Testing¶

In [65]:
from sklearn.metrics import accuracy_score

Obesity_accuracy = accuracy_score(y_test, y_pred)
print('The Accuracy of the scores is: : ', Obesity_accuracy)

The Accuracy of the scores is: :  0.9100946372239748
In [66]:
ObesityAccuracy_train_score = tr.score(X_train_ScaledObesityData, y_train)

ObesityAccuracy_test_score= tr.score(X_test_ScaledObesityData, y_test)


print('The Train score of the Decison Tree algorithm is : ' , ObesityAccuracy_train_score)

print('The Test score of the Decison Tree algorithm is : ' , ObesityAccuracy_test_score)

The Train score of the Decison Tree algorithm is :  1.0
The Test score of the Decison Tree algorithm is :  0.9100946372239748
In [67]:
from sklearn import  metrics

Obesity_prediction = 100*tr.score(X_test_ScaledObesityData, y_test)
print('Predictions for Decision Tree Classifier : \n', tr.predict(X_test_ScaledObesityData), '\n Accuracy:', Obesity_prediction, '%')

Predictions for Decision Tree Classifier : 
 [3 6 3 0 4 4 1 0 4 6 4 6 6 3 3 5 6 1 1 3 4 2 0 1 5 5 4 0 4 0 2 6 5 1 5 4 3
 2 4 3 6 0 3 3 0 1 1 5 0 1 1 1 0 5 2 2 1 0 3 4 6 6 0 0 2 5 1 1 2 0 0 1 6 1
 2 2 1 2 5 0 3 2 1 3 1 1 4 4 0 3 6 1 5 5 3 5 2 5 3 3 6 4 6 0 0 5 4 6 4 4 3
 5 4 6 6 0 5 5 1 2 6 5 1 2 2 2 4 5 6 4 0 0 4 4 2 2 4 4 0 4 5 4 0 5 6 5 6 3
 2 3 4 3 2 6 3 5 5 0 6 0 5 5 5 0 0 4 1 3 2 1 6 2 2 1 4 6 2 5 4 5 2 0 2 5 5
 6 0 4 6 6 1 4 2 4 2 2 2 2 5 0 1 1 1 6 2 5 2 1 5 5 6 5 5 1 0 6 4 1 3 2 5 1
 6 4 2 1 0 0 0 4 4 2 4 0 2 0 0 4 1 4 0 2 6 4 1 5 2 6 4 5 4 3 6 6 3 0 5 4 6
 0 4 6 4 3 4 4 5 2 2 2 6 1 3 2 2 1 4 2 5 0 2 4 4 0 0 1 3 2 6 5 6 6 6 1 1 0
 3 2 2 2 3 1 5 4 6 3 5 4 6 6 2 5 0 6 5 4 1 0 4 0 3 4 5 1 2 5 4 0 1 3 1 6 4
 0 4 6 5 0 6 1 0 2 0 6 3 5 0 0 3 3 5 3 5 0 3 3 2 6 2 5 0 1 0 0 4 1 3 1 0 5
 0 3 2 6 3 6 2 6 5 5 5 6 4 1 3 6 5 0 1 3 6 6 0 5 0 6 1 2 5 2 1 1 4 6 5 4 4
 3 6 6 1 4 5 0 2 3 1 6 1 4 1 1 0 2 4 1 5 0 5 4 3 4 2 4 5 5 0 1 6 2 2 5 2 2
 6 5 0 0 4 4 0 4 0 3 2 3 2 1 5 1 6 3 4 4 1 2 0 3 1 6 2 5 3 4 1 0 6 3 6 2 0
 3 6 3 1 0 0 3 0 5 0 0 4 4 3 5 5 2 0 3 2 4 1 2 3 4 1 2 0 2 2 0 2 1 5 4 5 4
 0 0 0 4 1 5 3 3 6 3 4 0 6 0 2 0 0 0 1 0 0 0 3 5 6 4 3 6 3 6 6 2 0 0 3 0 4
 0 3 6 2 3 1 0 1 0 4 6 5 6 0 0 1 5 4 2 3 3 5 0 2 2 2 2 2 1 6 1 0 0 0 6 2 6
 5 0 2 1 1 5 2 1 2 2 1 2 4 4 2 3 2 5 2 1 6 4 6 5 6 3 1 0 2 1 0 0 0 3 4 4 6
 6 5 2 1 1] 
 Accuracy: 91.00946372239747 %

Classification Report¶

In [68]:
from sklearn.metrics import classification_report

DecisionTree_report = classification_report(y_test, y_pred)
print(DecisionTree_report)

              precision    recall  f1-score   support

           0       0.91      0.94      0.93       106
           1       0.97      0.94      0.95        89
           2       0.99      1.00      0.99        97
           3       0.95      0.79      0.86        87
           4       0.88      0.93      0.91        87
           5       0.80      0.81      0.81        86
           6       0.87      0.93      0.90        82

    accuracy                           0.91       634
   macro avg       0.91      0.91      0.91       634
weighted avg       0.91      0.91      0.91       634

Confusion Matrix¶

In [69]:
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred))

[[100   3   0   0   3   0   0]
 [  4  84   1   0   0   0   0]
 [  0   0  97   0   0   0   0]
 [  1   0   0  69   6  11   0]
 [  5   0   0   1  81   0   0]
 [  0   0   0   3   2  70  11]
 [  0   0   0   0   0   6  76]]

Predicting New Data¶

In [70]:
X_test.iloc[80]
Out[70]:
Gender                             1.000000
Age                               21.000000
Height                             1.753578
Weight                            77.979170
family_history_with_overweight     0.000000
FAVC                               0.000000
FCVC                               2.273548
NCP                                2.390070
CAEC                               0.000000
SMOKE                              1.000000
CH2O                               1.648404
SCC                                1.000000
FAF                                0.874643
TUE                                1.102696
CALC                               0.000000
MTRANS                             0.000000
Name: 937, dtype: float64
In [71]:
tr.predict([X_test.iloc[80]])
Out[71]:
array([2], dtype=int64)
In [72]:
X_test.iloc[23]
Out[72]:
Gender                              0.000000
Age                                25.124595
Height                              1.771510
Weight                            113.207124
family_history_with_overweight      0.000000
FAVC                                0.000000
FCVC                                1.457758
NCP                                 3.000000
CAEC                                0.000000
SMOKE                               1.000000
CH2O                                2.020249
SCC                                 1.000000
FAF                                 1.556709
TUE                                 0.001330
CALC                                0.000000
MTRANS                              0.000000
Name: 1585, dtype: float64
In [73]:
tr.predict([X_test.iloc[23]])
Out[73]:
array([1], dtype=int64)
In [74]:
Checking_dataframe = pd.DataFrame({'Actual Value' : y_test, 'Predicted Value' : y_pred})
Checking_dataframe
Out[74]:
Actual Value Predicted Value
920 3 3
496 6 6
271 5 3
78 0 0
1021 4 4
... ... ...
622 6 6
437 5 5
1908 2 2
1779 1 1
1739 1 1

634 rows × 2 columns

Random forest classifier¶

In [75]:
from sklearn.ensemble import RandomForestClassifier

RandomForest_Obesity = RandomForestClassifier(n_jobs = -1, n_estimators = 100, max_features = 5, random_state = 45)
RandomForest_Obesity.fit(X_train_ScaledObesityData, y_train)
y_pred = RandomForest_Obesity.predict(X_test_ScaledObesityData)
y_pred
Out[75]:
array([3, 6, 5, 0, 4, 4, 1, 0, 4, 6, 4, 6, 6, 3, 3, 5, 6, 1, 0, 3, 4, 2,
       0, 1, 5, 5, 4, 0, 0, 0, 2, 6, 5, 1, 5, 4, 5, 2, 4, 3, 6, 0, 3, 3,
       0, 1, 1, 4, 0, 1, 1, 1, 0, 5, 2, 2, 1, 0, 3, 4, 6, 6, 0, 0, 2, 5,
       1, 1, 2, 0, 0, 1, 6, 1, 2, 2, 1, 2, 5, 0, 3, 2, 1, 3, 1, 1, 4, 4,
       0, 3, 6, 1, 5, 5, 3, 5, 2, 5, 3, 3, 6, 4, 5, 0, 4, 5, 4, 6, 4, 0,
       3, 3, 4, 6, 6, 0, 5, 5, 1, 2, 6, 3, 1, 2, 2, 2, 4, 5, 6, 4, 0, 0,
       4, 4, 2, 2, 4, 4, 0, 4, 6, 4, 0, 5, 6, 6, 6, 3, 2, 3, 4, 3, 2, 6,
       3, 5, 5, 0, 6, 0, 5, 5, 5, 0, 0, 4, 1, 3, 2, 1, 6, 2, 2, 0, 4, 6,
       2, 5, 4, 5, 2, 0, 2, 3, 3, 6, 0, 4, 6, 6, 1, 4, 2, 5, 2, 2, 2, 2,
       5, 0, 1, 1, 1, 6, 2, 5, 2, 1, 3, 5, 5, 5, 5, 1, 0, 5, 4, 1, 3, 2,
       5, 1, 6, 4, 2, 1, 0, 0, 0, 0, 4, 2, 4, 0, 2, 0, 0, 4, 1, 4, 0, 2,
       6, 4, 1, 5, 2, 6, 4, 6, 0, 3, 6, 6, 3, 0, 5, 4, 6, 0, 4, 6, 4, 3,
       4, 4, 5, 2, 2, 2, 6, 1, 3, 2, 2, 1, 4, 2, 5, 0, 2, 5, 4, 0, 0, 1,
       3, 2, 6, 5, 6, 6, 5, 1, 1, 0, 3, 2, 2, 2, 3, 1, 5, 4, 6, 3, 5, 3,
       6, 6, 2, 5, 5, 6, 5, 4, 1, 0, 4, 0, 3, 4, 5, 1, 2, 5, 4, 3, 1, 3,
       1, 5, 4, 0, 3, 6, 6, 0, 5, 1, 0, 2, 0, 6, 3, 5, 4, 5, 3, 3, 5, 3,
       5, 0, 3, 3, 2, 6, 2, 5, 0, 1, 0, 0, 4, 1, 3, 1, 0, 5, 0, 5, 2, 6,
       3, 6, 2, 6, 5, 5, 5, 6, 4, 1, 3, 6, 3, 5, 1, 3, 6, 6, 0, 5, 0, 6,
       1, 2, 5, 2, 1, 1, 4, 6, 5, 4, 4, 3, 6, 6, 1, 4, 5, 4, 2, 3, 1, 5,
       1, 4, 1, 1, 0, 2, 4, 1, 5, 0, 3, 4, 3, 4, 2, 4, 5, 5, 0, 1, 6, 2,
       2, 5, 2, 2, 6, 5, 0, 0, 4, 4, 0, 4, 0, 3, 2, 3, 2, 1, 5, 1, 6, 5,
       5, 4, 1, 2, 0, 5, 1, 6, 2, 6, 3, 3, 1, 0, 6, 3, 6, 2, 0, 3, 5, 3,
       1, 0, 0, 3, 0, 5, 0, 0, 4, 4, 3, 5, 5, 2, 0, 5, 2, 4, 1, 2, 3, 4,
       1, 2, 0, 2, 2, 0, 2, 1, 5, 4, 5, 5, 1, 0, 0, 4, 1, 5, 4, 3, 6, 3,
       5, 0, 5, 0, 2, 0, 0, 0, 1, 0, 0, 0, 3, 5, 6, 4, 3, 6, 3, 6, 6, 2,
       0, 0, 3, 0, 4, 0, 5, 6, 2, 5, 1, 0, 1, 0, 4, 6, 5, 6, 0, 0, 1, 5,
       4, 2, 3, 3, 5, 0, 2, 2, 2, 2, 2, 1, 6, 1, 0, 0, 0, 6, 2, 6, 5, 0,
       2, 1, 1, 5, 2, 1, 2, 2, 1, 2, 3, 4, 2, 3, 2, 5, 2, 1, 6, 4, 5, 5,
       6, 3, 1, 0, 2, 1, 0, 0, 0, 3, 4, 4, 6, 6, 5, 2, 1, 1], dtype=int64)

Accuracy¶

In [76]:
from sklearn.metrics import accuracy_score

RandomForest_ObesityAccuracy= accuracy_score(y_test, y_pred)
print("The accuracy of the score is: ", RandomForest_ObesityAccuracy)

The accuracy of the score is:  0.9416403785488959
In [77]:
RandomForest_train_score = RandomForest_Obesity.score(X_train_ScaledObesityData, y_train)

RandomForest_test_score= RandomForest_Obesity.score(X_test_ScaledObesityData, y_test)


print('Predictions for Random Forest Classifier Train Score is : ' , RandomForest_train_score)

print('Predictions for Random Forest Classifier Test Score is : : ' , RandomForest_test_score)

Predictions for Random Forest Classifier Train Score is :  1.0
Predictions for Random Forest Classifier Test Score is : :  0.9416403785488959
In [78]:
from sklearn import  metrics

RandomForest_accuracy = 100*RandomForest_Obesity.score(X_test_ScaledObesityData, y_test)
print(' Predictions for Random Forest Classifier : \n', RandomForest_Obesity.predict(X_test_ScaledObesityData), '\n Accuracy:', RandomForest_accuracy, '%')

 Predictions for Random Forest Classifier : 
 [3 6 5 0 4 4 1 0 4 6 4 6 6 3 3 5 6 1 0 3 4 2 0 1 5 5 4 0 0 0 2 6 5 1 5 4 5
 2 4 3 6 0 3 3 0 1 1 4 0 1 1 1 0 5 2 2 1 0 3 4 6 6 0 0 2 5 1 1 2 0 0 1 6 1
 2 2 1 2 5 0 3 2 1 3 1 1 4 4 0 3 6 1 5 5 3 5 2 5 3 3 6 4 5 0 4 5 4 6 4 0 3
 3 4 6 6 0 5 5 1 2 6 3 1 2 2 2 4 5 6 4 0 0 4 4 2 2 4 4 0 4 6 4 0 5 6 6 6 3
 2 3 4 3 2 6 3 5 5 0 6 0 5 5 5 0 0 4 1 3 2 1 6 2 2 0 4 6 2 5 4 5 2 0 2 3 3
 6 0 4 6 6 1 4 2 5 2 2 2 2 5 0 1 1 1 6 2 5 2 1 3 5 5 5 5 1 0 5 4 1 3 2 5 1
 6 4 2 1 0 0 0 0 4 2 4 0 2 0 0 4 1 4 0 2 6 4 1 5 2 6 4 6 0 3 6 6 3 0 5 4 6
 0 4 6 4 3 4 4 5 2 2 2 6 1 3 2 2 1 4 2 5 0 2 5 4 0 0 1 3 2 6 5 6 6 5 1 1 0
 3 2 2 2 3 1 5 4 6 3 5 3 6 6 2 5 5 6 5 4 1 0 4 0 3 4 5 1 2 5 4 3 1 3 1 5 4
 0 3 6 6 0 5 1 0 2 0 6 3 5 4 5 3 3 5 3 5 0 3 3 2 6 2 5 0 1 0 0 4 1 3 1 0 5
 0 5 2 6 3 6 2 6 5 5 5 6 4 1 3 6 3 5 1 3 6 6 0 5 0 6 1 2 5 2 1 1 4 6 5 4 4
 3 6 6 1 4 5 4 2 3 1 5 1 4 1 1 0 2 4 1 5 0 3 4 3 4 2 4 5 5 0 1 6 2 2 5 2 2
 6 5 0 0 4 4 0 4 0 3 2 3 2 1 5 1 6 5 5 4 1 2 0 5 1 6 2 6 3 3 1 0 6 3 6 2 0
 3 5 3 1 0 0 3 0 5 0 0 4 4 3 5 5 2 0 5 2 4 1 2 3 4 1 2 0 2 2 0 2 1 5 4 5 5
 1 0 0 4 1 5 4 3 6 3 5 0 5 0 2 0 0 0 1 0 0 0 3 5 6 4 3 6 3 6 6 2 0 0 3 0 4
 0 5 6 2 5 1 0 1 0 4 6 5 6 0 0 1 5 4 2 3 3 5 0 2 2 2 2 2 1 6 1 0 0 0 6 2 6
 5 0 2 1 1 5 2 1 2 2 1 2 3 4 2 3 2 5 2 1 6 4 5 5 6 3 1 0 2 1 0 0 0 3 4 4 6
 6 5 2 1 1] 
 Accuracy: 94.1640378548896 %

Classification Report¶

In [79]:
from sklearn.metrics import classification_report

RandomForest_Report = classification_report(y_test, y_pred)
print(RandomForest_Report)

              precision    recall  f1-score   support

           0       0.95      0.97      0.96       106
           1       0.99      0.96      0.97        89
           2       0.99      1.00      0.99        97
           3       0.97      0.85      0.91        87
           4       0.95      0.92      0.94        87
           5       0.80      0.93      0.86        86
           6       0.95      0.95      0.95        82

    accuracy                           0.94       634
   macro avg       0.94      0.94      0.94       634
weighted avg       0.95      0.94      0.94       634

Confusion Matrix¶

In [80]:
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred))

[[103   1   0   0   1   1   0]
 [  2  85   1   0   0   1   0]
 [  0   0  97   0   0   0   0]
 [  0   0   0  74   2  11   0]
 [  3   0   0   1  80   3   0]
 [  0   0   0   1   1  80   4]
 [  0   0   0   0   0   4  78]]

Predicting new data¶

In [81]:
X_test.iloc[20]
Out[81]:
Gender                             0.000000
Age                               34.543563
Height                             1.765188
Weight                            85.000000
family_history_with_overweight     0.000000
FAVC                               0.000000
FCVC                               2.694281
NCP                                3.000000
CAEC                               0.000000
SMOKE                              1.000000
CH2O                               2.653831
SCC                                1.000000
FAF                                1.965820
TUE                                0.891189
CALC                               1.000000
MTRANS                             1.000000
Name: 1014, dtype: float64
In [82]:
RandomForest_Obesity.predict([X_test.iloc[20]])
Out[82]:
array([1], dtype=int64)
In [83]:
X_test.iloc[40]
Out[83]:
Gender                             0.00
Age                               17.00
Height                             1.79
Weight                            57.00
family_history_with_overweight     0.00
FAVC                               0.00
FCVC                               2.00
NCP                                4.00
CAEC                               2.00
SMOKE                              1.00
CH2O                               2.00
SCC                                1.00
FAF                                2.00
TUE                                1.00
CALC                               1.00
MTRANS                             0.00
Name: 310, dtype: float64
In [84]:
RandomForest_Obesity.predict([X_test.iloc[40]])
Out[84]:
array([0], dtype=int64)
In [85]:
RfChecking_dataframe = pd.DataFrame({'Actual Value' : y_test, 'Predicted Value' : y_pred})
RfChecking_dataframe
Out[85]:
Actual Value Predicted Value
920 3 3
496 6 6
271 5 5
78 0 0
1021 4 4
... ... ...
622 6 6
437 5 5
1908 2 2
1779 1 1
1739 1 1

634 rows × 2 columns

6. CONCLUSION¶

In [86]:
pd.DataFrame(data={'Model': ['Decision Tree Classifier', 'Random Forest Classifier'], 'Accuracy %': [Obesity_accuracy, RandomForest_ObesityAccuracy]})
Out[86]:
Model Accuracy %
0 Decision Tree Classifier 0.910095
1 Random Forest Classifier 0.941640
In [87]:
plt.style.use("seaborn")

x = ['Decision Tree Classifier', 'Random Forest Classifier']

y = [Obesity_accuracy, RandomForest_ObesityAccuracy]

fig, ax = plt.subplots(figsize=(15,8))
sns.barplot(x=x,y=y, palette='inferno')
plt.ylabel('Model Accuracy')
plt.xticks(rotation=40)
plt.title('Compariam of the two models - Accuracy of the Models', fontsize=15, fontname='monospace', y=1.03);

The accuracy of Random Forest Classsifier is higher than Decision Tree Classifier in predicting obesity levels of individuals¶